Overview

Dataset statistics

Number of variables21
Number of observations21597
Missing cells6281
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric15
DateTime1
Boolean1
Categorical3
Text1

Alerts

price is highly overall correlated with sqft_living and 2 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 5 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 5 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with price and 3 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.3%)Imbalance
waterfront has 2376 (11.0%) missing valuesMissing
yr_renovated has 3842 (17.8%) missing valuesMissing
yr_renovated has 17011 (78.8%) zerosZeros

Reproduction

Analysis started2023-10-23 08:47:40.820110
Analysis finished2023-10-23 08:48:04.131658
Duration23.31 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct21420
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5804743 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:04.211824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1274039 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089005 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8767357 × 109
Coefficient of variation (CV)0.6280432
Kurtosis-1.2607499
Mean4.5804743 × 109
Median Absolute Deviation (MAD)2.4025303 × 109
Skewness0.24322552
Sum9.8924503 × 1013
Variance8.2756084 × 1018
MonotonicityNot monotonic
2023-10-23T11:48:04.333669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
8910500150 2
 
< 0.1%
7409700215 2
 
< 0.1%
1995200200 2
 
< 0.1%
9211500620 2
 
< 0.1%
1524079093 2
 
< 0.1%
4305200070 2
 
< 0.1%
1450100390 2
 
< 0.1%
7893805650 2
 
< 0.1%
109200390 2
 
< 0.1%
Other values (21410) 21576
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2023-10-23T11:48:04.444552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:04.561794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3622
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540296.57
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:04.686941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1322000
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7622000
Interquartile range (IQR)323000

Descriptive statistics

Standard deviation367368.14
Coefficient of variation (CV)0.67993794
Kurtosis34.541359
Mean540296.57
Median Absolute Deviation (MAD)150000
Skewness4.0233647
Sum1.1668785 × 1010
Variance1.3495935 × 1011
MonotonicityNot monotonic
2023-10-23T11:48:04.811615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (3612) 20097
93.1%
ValueCountFrequency (%)
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
89000 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4500000 1
< 0.1%
4490000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3732
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:04.911848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92629889
Coefficient of variation (CV)0.27460539
Kurtosis49.821835
Mean3.3732
Median Absolute Deviation (MAD)1
Skewness2.0236412
Sum72851
Variance0.85802964
MonotonicityNot monotonic
2023-10-23T11:48:05.008756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.9%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 196
 
0.9%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 196
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.9%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.9%
3 9824
45.5%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1158263
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:05.101841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7689843
Coefficient of variation (CV)0.36344397
Kurtosis1.2793153
Mean2.1158263
Median Absolute Deviation (MAD)0.5
Skewness0.51970928
Sum45695.5
Variance0.59133685
MonotonicityNot monotonic
2023-10-23T11:48:05.218065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.5 5377
24.9%
1 3851
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1445
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (19) 641
 
3.0%
ValueCountFrequency (%)
0.5 4
 
< 0.1%
0.75 71
 
0.3%
1 3851
17.8%
1.25 9
 
< 0.1%
1.5 1445
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5377
24.9%
2.75 1185
 
5.5%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1034
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2080.3219
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:05.346926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3760
Maximum13540
Range13170
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.10613
Coefficient of variation (CV)0.44132889
Kurtosis5.252102
Mean2080.3219
Median Absolute Deviation (MAD)540
Skewness1.4732155
Sum44928711
Variance842918.86
MonotonicityNot monotonic
2023-10-23T11:48:05.476987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1024) 20302
94.0%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
390 1
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
470 2
< 0.1%
480 2
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9776
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099.409
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:05.601567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800.8
Q15040
median7618
Q310685
95-th percentile43307.2
Maximum1651359
Range1650839
Interquartile range (IQR)5645

Descriptive statistics

Standard deviation41412.637
Coefficient of variation (CV)2.7426661
Kurtosis285.49581
Mean15099.409
Median Absolute Deviation (MAD)2618
Skewness13.072604
Sum3.2610193 × 108
Variance1.7150065 × 109
MonotonicityNot monotonic
2023-10-23T11:48:05.723661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 119
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9766) 19803
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4940964
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:05.821979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53968279
Coefficient of variation (CV)0.36121015
Kurtosis-0.49106576
Mean1.4940964
Median Absolute Deviation (MAD)0.5
Skewness0.61449698
Sum32268
Variance0.29125751
MonotonicityNot monotonic
2023-10-23T11:48:05.924797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10673
49.4%
2 8235
38.1%
1.5 1910
 
8.8%
3 611
 
2.8%
2.5 161
 
0.7%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
1 10673
49.4%
1.5 1910
 
8.8%
2 8235
38.1%
2.5 161
 
0.7%
3 611
 
2.8%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
3.5 7
 
< 0.1%
3 611
 
2.8%
2.5 161
 
0.7%
2 8235
38.1%
1.5 1910
 
8.8%
1 10673
49.4%

waterfront
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2376
Missing (%)11.0%
Memory size42.3 KiB
False
19075 
True
 
146
(Missing)
2376 
ValueCountFrequency (%)
False 19075
88.3%
True 146
 
0.7%
(Missing) 2376
 
11.0%
2023-10-23T11:48:06.011671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing63
Missing (%)0.3%
Memory size168.9 KiB
NONE
19422 
AVERAGE
 
957
GOOD
 
508
FAIR
 
330
EXCELLENT
 
317

Length

Max length9
Median length4
Mean length4.2069286
Min length4

Characters and Unicode

Total characters90592
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNONE
2nd rowNONE
3rd rowNONE
4th rowNONE
5th rowNONE

Common Values

ValueCountFrequency (%)
NONE 19422
89.9%
AVERAGE 957
 
4.4%
GOOD 508
 
2.4%
FAIR 330
 
1.5%
EXCELLENT 317
 
1.5%
(Missing) 63
 
0.3%

Length

2023-10-23T11:48:06.105790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T11:48:06.204797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
none 19422
90.2%
average 957
 
4.4%
good 508
 
2.4%
fair 330
 
1.5%
excellent 317
 
1.5%

Most occurring characters

ValueCountFrequency (%)
N 39161
43.2%
E 22287
24.6%
O 20438
22.6%
A 2244
 
2.5%
G 1465
 
1.6%
R 1287
 
1.4%
V 957
 
1.1%
L 634
 
0.7%
D 508
 
0.6%
F 330
 
0.4%
Other values (4) 1281
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 90592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 39161
43.2%
E 22287
24.6%
O 20438
22.6%
A 2244
 
2.5%
G 1465
 
1.6%
R 1287
 
1.4%
V 957
 
1.1%
L 634
 
0.7%
D 508
 
0.6%
F 330
 
0.4%
Other values (4) 1281
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 90592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 39161
43.2%
E 22287
24.6%
O 20438
22.6%
A 2244
 
2.5%
G 1465
 
1.6%
R 1287
 
1.4%
V 957
 
1.1%
L 634
 
0.7%
D 508
 
0.6%
F 330
 
0.4%
Other values (4) 1281
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 39161
43.2%
E 22287
24.6%
O 20438
22.6%
A 2244
 
2.5%
G 1465
 
1.6%
R 1287
 
1.4%
V 957
 
1.1%
L 634
 
0.7%
D 508
 
0.6%
F 330
 
0.4%
Other values (4) 1281
 
1.4%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
Average
14020 
Good
5677 
Very Good
1701 
Fair
 
170
Poor
 
29

Length

Max length9
Median length7
Mean length6.3412974
Min length4

Characters and Unicode

Total characters136953
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage
2nd rowAverage
3rd rowAverage
4th rowVery Good
5th rowAverage

Common Values

ValueCountFrequency (%)
Average 14020
64.9%
Good 5677
26.3%
Very Good 1701
 
7.9%
Fair 170
 
0.8%
Poor 29
 
0.1%

Length

2023-10-23T11:48:06.316432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T11:48:06.419586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
average 14020
60.2%
good 7378
31.7%
very 1701
 
7.3%
fair 170
 
0.7%
poor 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 29741
21.7%
r 15920
11.6%
o 14814
10.8%
a 14190
10.4%
A 14020
10.2%
v 14020
10.2%
g 14020
10.2%
G 7378
 
5.4%
d 7378
 
5.4%
V 1701
 
1.2%
Other values (5) 3771
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111954
81.7%
Uppercase Letter 23298
 
17.0%
Space Separator 1701
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29741
26.6%
r 15920
14.2%
o 14814
13.2%
a 14190
12.7%
v 14020
12.5%
g 14020
12.5%
d 7378
 
6.6%
y 1701
 
1.5%
i 170
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
A 14020
60.2%
G 7378
31.7%
V 1701
 
7.3%
F 170
 
0.7%
P 29
 
0.1%
Space Separator
ValueCountFrequency (%)
1701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135252
98.8%
Common 1701
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29741
22.0%
r 15920
11.8%
o 14814
11.0%
a 14190
10.5%
A 14020
10.4%
v 14020
10.4%
g 14020
10.4%
G 7378
 
5.5%
d 7378
 
5.5%
V 1701
 
1.3%
Other values (4) 2070
 
1.5%
Common
ValueCountFrequency (%)
1701
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29741
21.7%
r 15920
11.6%
o 14814
10.8%
a 14190
10.4%
A 14020
10.2%
v 14020
10.2%
g 14020
10.2%
G 7378
 
5.4%
d 7378
 
5.4%
V 1701
 
1.2%
Other values (5) 3771
 
2.8%

grade
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
7 Average
8974 
8 Good
6065 
9 Better
2615 
6 Low Average
2038 
10 Very Good
1134 
Other values (6)
 
771

Length

Max length13
Median length12
Mean length8.5886929
Min length5

Characters and Unicode

Total characters185490
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row7 Average
2nd row7 Average
3rd row6 Low Average
4th row7 Average
5th row8 Good

Common Values

ValueCountFrequency (%)
7 Average 8974
41.6%
8 Good 6065
28.1%
9 Better 2615
 
12.1%
6 Low Average 2038
 
9.4%
10 Very Good 1134
 
5.3%
11 Excellent 399
 
1.8%
5 Fair 242
 
1.1%
12 Luxury 89
 
0.4%
4 Low 27
 
0.1%
13 Mansion 13
 
0.1%

Length

2023-10-23T11:48:06.538649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
average 11012
23.8%
7 8974
19.4%
good 7199
15.5%
8 6065
13.1%
9 2615
 
5.6%
better 2615
 
5.6%
low 2065
 
4.5%
6 2038
 
4.4%
10 1134
 
2.4%
very 1134
 
2.4%
Other values (11) 1515
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e 29186
15.7%
24769
13.4%
o 16478
8.9%
r 15093
 
8.1%
a 11267
 
6.1%
A 11012
 
5.9%
v 11012
 
5.9%
g 11012
 
5.9%
7 8974
 
4.8%
d 7199
 
3.9%
Other values (27) 39488
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112720
60.8%
Space Separator 24769
 
13.4%
Uppercase Letter 24769
 
13.4%
Decimal Number 23232
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29186
25.9%
o 16478
14.6%
r 15093
13.4%
a 11267
 
10.0%
v 11012
 
9.8%
g 11012
 
9.8%
d 7199
 
6.4%
t 5629
 
5.0%
w 2065
 
1.8%
y 1223
 
1.1%
Other values (7) 2556
 
2.3%
Decimal Number
ValueCountFrequency (%)
7 8974
38.6%
8 6065
26.1%
9 2615
 
11.3%
6 2038
 
8.8%
1 2034
 
8.8%
0 1134
 
4.9%
5 242
 
1.0%
2 89
 
0.4%
4 27
 
0.1%
3 14
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 11012
44.5%
G 7199
29.1%
B 2615
 
10.6%
L 2154
 
8.7%
V 1134
 
4.6%
E 399
 
1.6%
F 242
 
1.0%
M 13
 
0.1%
P 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
24769
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 137489
74.1%
Common 48001
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29186
21.2%
o 16478
12.0%
r 15093
11.0%
a 11267
 
8.2%
A 11012
 
8.0%
v 11012
 
8.0%
g 11012
 
8.0%
d 7199
 
5.2%
G 7199
 
5.2%
t 5629
 
4.1%
Other values (16) 12402
9.0%
Common
ValueCountFrequency (%)
24769
51.6%
7 8974
 
18.7%
8 6065
 
12.6%
9 2615
 
5.4%
6 2038
 
4.2%
1 2034
 
4.2%
0 1134
 
2.4%
5 242
 
0.5%
2 89
 
0.2%
4 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29186
15.7%
24769
13.4%
o 16478
8.9%
r 15093
 
8.1%
a 11267
 
6.1%
A 11012
 
5.9%
v 11012
 
5.9%
g 11012
 
5.9%
7 8974
 
4.8%
d 7199
 
3.9%
Other values (27) 39488
21.3%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct942
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.5968
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:06.658155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation827.75976
Coefficient of variation (CV)0.4627984
Kurtosis3.4055198
Mean1788.5968
Median Absolute Deviation (MAD)450
Skewness1.4474342
Sum38628326
Variance685186.22
MonotonicityNot monotonic
2023-10-23T11:48:06.773741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (932) 19708
91.3%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
390 1
 
< 0.1%
410 1
 
< 0.1%
420 2
< 0.1%
430 1
 
< 0.1%
440 1
 
< 0.1%
460 1
 
< 0.1%
470 2
< 0.1%
480 4
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%
Distinct304
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:06.963262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.8160393
Min length1

Characters and Unicode

Total characters82415
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row400.0
3rd row0.0
4th row910.0
5th row0.0
ValueCountFrequency (%)
0.0 12826
59.4%
454
 
2.1%
600.0 217
 
1.0%
500.0 209
 
1.0%
700.0 208
 
1.0%
800.0 201
 
0.9%
400.0 184
 
0.9%
1000.0 148
 
0.7%
900.0 142
 
0.7%
300.0 142
 
0.7%
Other values (294) 6866
31.8%
2023-10-23T11:48:07.261899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44732
54.3%
. 21143
25.7%
1 3208
 
3.9%
5 1818
 
2.2%
2 1751
 
2.1%
4 1708
 
2.1%
8 1639
 
2.0%
6 1602
 
1.9%
7 1554
 
1.9%
3 1522
 
1.8%
Other values (2) 1738
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60818
73.8%
Other Punctuation 21597
 
26.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44732
73.6%
1 3208
 
5.3%
5 1818
 
3.0%
2 1751
 
2.9%
4 1708
 
2.8%
8 1639
 
2.7%
6 1602
 
2.6%
7 1554
 
2.6%
3 1522
 
2.5%
9 1284
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 21143
97.9%
? 454
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 82415
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44732
54.3%
. 21143
25.7%
1 3208
 
3.9%
5 1818
 
2.2%
2 1751
 
2.1%
4 1708
 
2.1%
8 1639
 
2.0%
6 1602
 
1.9%
7 1554
 
1.9%
3 1522
 
1.8%
Other values (2) 1738
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44732
54.3%
. 21143
25.7%
1 3208
 
3.9%
5 1818
 
2.2%
2 1751
 
2.1%
4 1708
 
2.1%
8 1639
 
2.0%
6 1602
 
1.9%
7 1554
 
1.9%
3 1522
 
1.8%
Other values (2) 1738
 
2.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9997
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:07.394233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.375234
Coefficient of variation (CV)0.014903723
Kurtosis-0.65769443
Mean1970.9997
Median Absolute Deviation (MAD)23
Skewness-0.46944998
Sum42567680
Variance862.90438
MonotonicityNot monotonic
2023-10-23T11:48:07.522122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 453
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 420
 
1.9%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17313
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 453
2.1%

yr_renovated
Real number (ℝ)

MISSING  ZEROS 

Distinct70
Distinct (%)0.4%
Missing3842
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean83.636778
Minimum0
Maximum2015
Zeros17011
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:07.641915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation399.94641
Coefficient of variation (CV)4.7819443
Kurtosis18.919543
Mean83.636778
Median Absolute Deviation (MAD)0
Skewness4.5733852
Sum1484971
Variance159957.13
MonotonicityNot monotonic
2023-10-23T11:48:07.767478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17011
78.8%
2014 73
 
0.3%
2013 31
 
0.1%
2003 31
 
0.1%
2007 30
 
0.1%
2000 29
 
0.1%
2005 29
 
0.1%
2004 22
 
0.1%
1990 22
 
0.1%
2009 21
 
0.1%
Other values (60) 456
 
2.1%
(Missing) 3842
 
17.8%
ValueCountFrequency (%)
0 17011
78.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 1
 
< 0.1%
1948 1
 
< 0.1%
1950 1
 
< 0.1%
1951 1
 
< 0.1%
1953 1
 
< 0.1%
ValueCountFrequency (%)
2015 14
 
0.1%
2014 73
0.3%
2013 31
0.1%
2012 8
 
< 0.1%
2011 9
 
< 0.1%
2010 15
 
0.1%
2009 21
 
0.1%
2008 15
 
0.1%
2007 30
0.1%
2006 20
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.952
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:07.891872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.513072
Coefficient of variation (CV)0.00054561776
Kurtosis-0.85400486
Mean98077.952
Median Absolute Deviation (MAD)42
Skewness0.40532219
Sum2.1181895 × 109
Variance2863.6489
MonotonicityNot monotonic
2023-10-23T11:48:08.022199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 589
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 547
 
2.5%
98034 545
 
2.5%
98118 507
 
2.3%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16100
74.5%
ValueCountFrequency (%)
98001 361
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5033
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560093
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:08.141648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.5718
Q347.678
95-th percentile47.7497
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.13855177
Coefficient of variation (CV)0.0029131938
Kurtosis-0.67579021
Mean47.560093
Median Absolute Deviation (MAD)0.1049
Skewness-0.48552159
Sum1027155.3
Variance0.019196592
MonotonicityNot monotonic
2023-10-23T11:48:08.261914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6846 17
 
0.1%
47.5322 17
 
0.1%
47.6624 17
 
0.1%
47.6711 16
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.6647 15
 
0.1%
47.6904 15
 
0.1%
47.686 15
 
0.1%
Other values (5023) 21436
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct751
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21398
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21597
Negative (%)100.0%
Memory size168.9 KiB
2023-10-23T11:48:08.391772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.231
Q3-122.125
95-th percentile-121.9798
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14072353
Coefficient of variation (CV)-0.001151452
Kurtosis1.0521203
Mean-122.21398
Median Absolute Deviation (MAD)0.101
Skewness0.88488834
Sum-2639455.4
Variance0.019803112
MonotonicityNot monotonic
2023-10-23T11:48:08.511652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 115
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.372 99
 
0.5%
-122.363 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (741) 20586
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.6203
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:08.631136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.23047
Coefficient of variation (CV)0.34492271
Kurtosis1.5917328
Mean1986.6203
Median Absolute Deviation (MAD)410
Skewness1.1068754
Sum42905039
Variance469540.8
MonotonicityNot monotonic
2023-10-23T11:48:08.741992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 180
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 164
 
0.8%
Other values (767) 19835
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8682
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12758.284
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-23T11:48:08.861572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2002.4
Q15100
median7620
Q310083
95-th percentile37045.2
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27274.442
Coefficient of variation (CV)2.137783
Kurtosis151.39566
Mean12758.284
Median Absolute Deviation (MAD)2505
Skewness9.524362
Sum2.7554065 × 108
Variance7.4389518 × 108
MonotonicityNot monotonic
2023-10-23T11:48:08.991854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 356
 
1.6%
6000 288
 
1.3%
7200 210
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8672) 19582
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2023-10-23T11:48:01.991560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.168180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.471863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.771945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.021538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.281760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.578292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.846697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.600571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.895617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.221950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.561617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.219315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.481901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.741874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.074239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.265777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.560136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.859359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.106655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.372808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.661556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.931899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.701607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.975427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.318416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.642748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.300463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.564329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.822142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.161575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.356858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.650283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.941870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.191850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.461697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.748504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:50.041707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.791533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.065660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.411804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.051873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.389562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.651699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.912456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.244237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.441669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.735159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.026707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.274439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.543378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.831631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.571978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.878668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.156853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.501631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.136675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.471687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.735434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.996664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.322710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.528914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.824605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.111794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.361512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.631690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.913796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.658233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.972599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.255584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.597672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.222774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.557311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.820408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.075323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.410388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.615769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.912577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.196998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.451789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.712753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.001774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.751925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.057880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.351617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.695994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.319845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.646546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.906465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.166920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.493019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.706687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.001965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.283021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.531934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.801943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.089354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.831653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.150448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.445134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.783734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.431837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.730348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.991970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.244295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.564006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.792066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.086478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.361670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.619817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.886880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.170039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.914678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.231536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.537964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.875559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.517483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.811799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.064719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.325272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.641842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.870872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.166510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.441639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.691736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.961643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.247512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:51.991950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.309930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.622310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.951535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.598966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.890380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.146891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.406965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.724229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:42.956936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.251681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.525095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.781572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.056719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.335169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.084903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.391844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.701837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.041735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.688672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.975655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.231932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.491795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.809842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.042754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.345485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.612785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.871893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.151488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.425843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.172870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.480684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.796464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.131611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.780717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.064809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.321968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.578375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.896910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.131893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.441562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.703345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:46.961757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.244127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.518850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.263566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.571852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.895664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.229722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.875578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.158082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.411815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.668665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:02.973394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.221776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.528504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.784891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.046920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.331615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.603328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.348986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.654501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:54.981982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.314517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:57.962080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.240096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.494004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.750695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:03.057529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.311919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.611576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.861811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.126734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.413375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.686055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.440585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.734221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.064223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.396655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.048709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.323370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.580021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.831649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:03.131708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:43.396621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:44.696258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:45.946847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:47.208684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:48.499784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:49.768133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:52.520240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:53.816782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:55.141822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:56.481748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:58.135213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:47:59.405132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:00.662638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-23T11:48:01.912578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-23T11:48:09.087490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorssqft_aboveyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15waterfrontviewconditiongrade
id1.0000.0040.0060.0150.002-0.1170.0190.0040.027-0.013-0.005-0.0040.0070.000-0.1150.0060.0290.0300.037
price0.0041.0000.3440.4970.6440.0750.3220.5420.1020.106-0.0090.4560.0640.5720.0630.3330.2070.0230.295
bedrooms0.0060.3441.0000.5210.6480.2170.2280.5400.1810.014-0.168-0.0220.1930.4440.2020.0160.0380.0120.081
bathrooms0.0150.4970.5211.0000.7460.0690.5480.6910.5680.042-0.2050.0080.2620.5710.0640.1090.1110.1220.310
sqft_living0.0020.6440.6480.7461.0000.3050.4010.8430.3530.053-0.2070.0310.2850.7470.2850.1510.1470.0560.369
sqft_lot-0.1170.0750.2170.0690.3051.000-0.2340.273-0.0370.005-0.319-0.1220.3710.3600.9220.0220.0410.0400.042
floors0.0190.3220.2280.5480.401-0.2341.0000.5990.5510.010-0.0620.0240.1490.306-0.2310.0170.0220.1780.245
sqft_above0.0040.5420.5400.6910.8430.2730.5991.0000.4720.029-0.279-0.0260.3860.6970.2550.0840.0890.1060.359
yr_built0.0270.1020.1810.5680.353-0.0370.5510.4721.000-0.216-0.317-0.1260.4130.336-0.0160.0350.0420.2480.195
yr_renovated-0.0130.1060.0140.0420.0530.0050.0100.029-0.2161.0000.0680.028-0.080-0.0040.0050.0850.1060.0690.019
zipcode-0.005-0.009-0.168-0.205-0.207-0.319-0.062-0.279-0.3170.0681.0000.249-0.577-0.287-0.3260.0780.0740.0740.110
lat-0.0040.456-0.0220.0080.031-0.1220.024-0.026-0.1260.0280.2491.000-0.1430.027-0.1160.0330.0680.0570.116
long0.0070.0640.1930.2620.2850.3710.1490.3860.413-0.080-0.577-0.1431.0000.3810.3730.0920.0850.0810.101
sqft_living150.0000.5720.4440.5710.7470.3600.3060.6970.336-0.004-0.2870.0270.3811.0000.3660.0920.1470.0620.326
sqft_lot15-0.1150.0630.2020.0640.2850.922-0.2310.255-0.0160.005-0.326-0.1160.3730.3661.0000.0000.0350.0130.038
waterfront0.0060.3330.0160.1090.1510.0220.0170.0840.0350.0850.0780.0330.0920.0920.0001.0000.5990.0190.129
view0.0290.2070.0380.1110.1470.0410.0220.0890.0420.1060.0740.0680.0850.1470.0350.5991.0000.0250.142
condition0.0300.0230.0120.1220.0560.0400.1780.1060.2480.0690.0740.0570.0810.0620.0130.0190.0251.0000.128
grade0.0370.2950.0810.3100.3690.0420.2450.3590.1950.0190.1100.1160.1010.3260.0380.1290.1420.1281.000

Missing values

2023-10-23T11:48:03.651866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-23T11:48:03.899862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-23T11:48:04.066748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052010/13/2014221900.031.00118056501.0NaNNONEAverage7 Average11800.019550.09817847.5112-122.25713405650
1641410019212/9/2014538000.032.25257072422.0NONONEAverage7 Average2170400.019511991.09812547.7210-122.31916907639
256315004002/25/2015180000.021.00770100001.0NONONEAverage6 Low Average7700.01933NaN9802847.7379-122.23327208062
3248720087512/9/2014604000.043.00196050001.0NONONEVery Good7 Average1050910.019650.09813647.5208-122.39313605000
419544005102/18/2015510000.032.00168080801.0NONONEAverage8 Good16800.019870.09807447.6168-122.04518007503
572375503105/12/20141230000.044.5054201019301.0NONONEAverage11 Excellent38901530.020010.09805347.6561-122.0054760101930
613214000606/27/2014257500.032.25171568192.0NONONEAverage7 Average1715?19950.09800347.3097-122.32722386819
720080002701/15/2015291850.031.50106097111.0NONaNAverage7 Average10600.019630.09819847.4095-122.31516509711
824146001264/15/2015229500.031.00178074701.0NONONEAverage7 Average1050730.019600.09814647.5123-122.33717808113
937935001603/12/2015323000.032.50189065602.0NONONEAverage7 Average18900.020030.09803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2158778521400408/25/2014507250.032.50227055362.0NaNNONEAverage8 Good22700.020030.09806547.5389-121.88122705731
2158898342013671/26/2015429000.032.00149011263.0NONONEAverage8 Good14900.020140.09814447.5699-122.28814001230
21589344890021010/14/2014610685.042.50252060232.0NONaNAverage9 Better25200.020140.09805647.5137-122.16725206023
2159079360004293/26/20151010000.043.50351072002.0NONONEAverage9 Better2600910.020090.09813647.5537-122.39820506200
2159129978000212/19/2015475000.032.50131012942.0NONONEAverage8 Good1180130.020080.09811647.5773-122.40913301265
215922630000185/21/2014360000.032.50153011313.0NONONEAverage8 Good15300.020090.09810347.6993-122.34615301509
2159366000601202/23/2015400000.042.50231058132.0NONONEAverage8 Good23100.020140.09814647.5107-122.36218307200
2159415233001416/23/2014402101.020.75102013502.0NONONEAverage7 Average10200.020090.09814447.5944-122.29910202007
215952913101001/16/2015400000.032.50160023882.0NaNNONEAverage8 Good16000.020040.09802747.5345-122.06914101287
21596152330015710/15/2014325000.020.75102010762.0NONONEAverage7 Average10200.020080.09814447.5941-122.29910201357